Here, we should mention that some successful systems combined (Taku Kudoh and Yuji Matsumoto, 2001) or features (Zhang et al.,02) enhanced have been better than ours.

it is not a fair comparison to our system since it is reasonable to believe that we can achieve appreciable improvement in the similar approaches.


In fact, it is probably not possible to divide senses, much less words, into independent referential and syntactic uses.

we can still say that function words indicate 'linguistically' significant relations.


Verb classifications have been used to support a number of practical tasks and applications, such as parsing, information extraction, question-answering, and machine translation.

large-scale exploitation of verb classes in real-world or domain-sensitive tasks has not been possible because existing manually built classifications are incomprehensive.


This shows the benefit of following the recent developments in the ML community.

semi-supervised approaches have not been used for the task yet (except for the sub-task of feature selection) although they are wellknown in the NLP community and would combine the benefits of supervised and unsupervised approaches (Abney, 2008).


Thus, the semantic ATR is proved to be more effective than the syntactic DOR.

the ATR is not quite useful in Korean, as shown in (14).


The first reading triggered by (27a), meaning 'Youyou hit Taotao and Youyou died', should be ruled out.

the reason for this ill-formedness is not clearly mentioned.


Free phone recognition, in principle, could support a CAPT tool in providing detailed phone-level feedback to a learner.

highly accurate free phone recognition is still not possible for native-speech, and is only expected to be even more difficult for the interlanguage of second language learners.


When we check the top-5 and top-10 sentences, the acceptable rate rises to 80%.

the overall rate of perfect sentences is much smaller than that of the acceptable level, indicating that our system can generate poetic sentences that roughly meet the rules but is still hard to generate excellent ones.


BLEU (Papineni et al., 2002) captures lexical variation by the use of multiple reference translations.

this has been shown to be a rather problematic solution: even if numerous human translations of the same original text are available, which is rarely the case, their use poses additional problems during evaluation.


In the same way, the other pair '(which-6 is-7)' is obtained, too.

the problem is that 'which-6' is not the true subject capable to evaluate if the subject-verb is in agreement, the true one should be 'hook-5'.


With lexical file information, one can derive a flat semantic classification, which in some situation is more convenient to use.

it is not always easy to classify a named entity into one of the 26 nominal lexicographer files because of the systematic ambiguity between LOCATION and OBJECT (e.g., island), GROUP and ARTIFACT (e.g., bank), ANIMAL and FOOD (e.g., fish), just to name a few.


Editorial categories indicating stub articles are not included as our training features.

even underdeveloped entries may still contain relevant information for classifying the title, we do not completely exclude these articles at training time.


For example, if the type of the mention of the focus person is "nominal" in the ACE output, the mention string is assigned as "occupation" in the WePS output.

the EXERT system provides only six WePS attributes (relations): nationality, affiliation, school, relatives, occupation and other name.


It beats the top system in the WePS 2009 AE evaluation (Sekine & Artiles, 2009): 12.22.

the low F score also indicates that personal information extraction from web data is still a big challenge.


As shown in Table 5, we find that the performances of "birth place" and "date of birth" improve significantly after incorporating hybrid NER.

in Table 3, we know that "birth" information appears almost evenly in both kinds of fragment, especially for "date of birth."


Single-label classification assumes that the pre-defined labels are mutually exclusive and each instance belongs to one label only.

this assumption is often invalid in emotion classification.


For example, a sentence containing the word "joyful" indicates the presence of "happiness" emotion.

this intuitive approach cannot work well because of the following reasons: 1) As explained above, it is impossible to collect all emotion keywords not only because the size of emotion expressions is very large (Mishne, 2005) but also because emotion expressions evolve from time to time.


Due to its inherent capability of producing interpretable and semantically coherent topics, LDA has been widely used in text analysis and shown promising performance in tasks like topic mining, browsing, and accessing document similarity.

when LDA is applied to text classification tasks, it is often used as only a dimension reduction step to extract features for consecutive discriminative models (e.g.


As our discussion, a more straightforward formulation is to directly associate λ to the topic mixture θ rather than the empirical topic frequenciesz in the proposed model.

this association makes the derivation of training algorithm much more complex.


A concern about the single Gaussian per category assumption is that single Gaussian cannot deal with complex decision boundary as the embeddings of documents may not be linearly separable.

if the number of topics is not too small (e.g.


It took more than two weeks to train sLDA classifiers.

it usually takes a day or two to train our model.


Attempts to embed context-rich approaches from WSD methods into SMT systems to enhance lexical selection did not lead to any improvement in translation quality (Carpuat and Wu, 2005).

more recent approaches of integrating state-of-theart WSD methods into SMT to improve the overall translation quality have met with more success (Carpuat and Wu, 2007;Chan et al., 2007;Màrquez, 2007, 2009).


Traditional shallow semantic parsing systems for chunk analysis (Kudoh and Matsumoto, 2001;Zhan etal., 2002), which employ machine learning method, focus on the selection of features and their variety combinations to train a statistical model (Carreras and Marquez, 2005).

we usually have no idea of what a good model is like, neither do we know which features to select.


Knowledge of any discipline is provided on on-line forums or on certain Web pages (e.g., WIKIPEDIA 1 ) by the collaborative effort of a number of domain experts.

more and more Internet users author their personal experiences on the Web.


After word segmentation, our training data consisted of approximately 2M Chinese words.

our semantic word classes were based on 同義詞詞林 (Chinese synonym thesaurus), and we employed Zhang's MaxEnt toolkit 3 to tune the feature weights described in the training procedure.


Encouragingly, giving more weights to sentences containing adverbs by means of replicating their n-gram and semantic features boosted the precision to 46.34, achieving a relative gain of 2.5% over the strong MaxEnt baseline.

our model may be used to provide a list of rank suggestion for a review.


Xiao and McEnery (2005:70) compared two reference corpora, the 100-million-word British National Corpus and the one million-word Freiburg-LOB Corpus, and achieved almost identical key word lists, thus concluding that the size of the reference corpus is not very important in making a key word list.

wendy and Corbett (2009) point out that "key words are more sensitive to the size of a corpus, for example, longer texts will contain fewer unique occurrences of particular words".


To prevent the error propagation, they regarded the labeling results as features of the subsequent parsing stage instead of reducing the child words.

this weakens the strength that neighboring parsing can provide.


The algorithm described above stops the parsing process if there is no reduce label "r" in the current layer.

sometimes the fact is that the parser quits so early while the tree is not well formed yet at that point.


The Classifier Splitting heuristic strategy and SVM speeding up methods (Goldberg and Elhadad, 2008) are gold choices to accelerate these implementations.

even considering these cases, the parsing speed of the proposed LDParsers (up to 480 English words per second) is still desirable.


(2007) did to find the optimal combination of dependency relations.

they scored all the graph space as what graph-based models do while we focalized it in a parsing layer for the sake of efficiency.


Therefore, there is a growing demand for technologies that enable us to obtain reliable information.

it would be almost impossible to automatically judge the accuracy of information presented on the Web.


For example, let us consider the following two pairs of statements:   The pair of statements (a1) and (a2) appear contradictory.

the pair of statements (b1) and (b2) are not logically contradictory because the statements are described in different contexts, namely, air pollution and global warming.


In the top 30 results, if the weight of the reason link is set to an optimum value, the precision in each topic is considerably improved to 76.7% and 56.7% respectively.

at weights of reason link lower than 10, the precision in the topic of "LASIK operation is painful" is lower than the ration of answer passages.


This is because the TextRank algorithm gives high significance to passages including frequent words, and the number of frequent words in the answer passages of the topic "LASIK operation is painful" is fewer than the one in the other topic.

increasing the weight of reason link can improve results in such topic.


Anaphors like Icelandic LDA in subjunctive complements, LD-bound Chinese 'ziji' (according to Liu 2001, though not Pollard andXue 2001) and English exempt anaphors fall into this category and show the following properties: 1) The LDA need not have c-commanding antecedent; 2) The LDAs have an antecedent outside the sentence; 3) Both sloppy and strict readings are allowed in VP ellipsis when the anaphor is LD-bound.

unlike Type II anaphors, Type III anaphors behave like a core anaphor in a local domain.


The result with 'caki' seems to show that logophoricity is not irrelevant, but that the Sells' hierarchy is not supported.

the sentences with less logophoric 10 A series of Paired Sample T-Tests revealed that the mean difference between less logophoric antecedent and other logophoric antecedents was significant (SOURCE vs. less logophoric (p< .0141); SELF vs. less logophoric (p< .0001); PIVOT vs. less logophoric (p< .0561)).


This algorithm is applied to a coreference resolution (Yang and Su, 2007).

these methods consider only single sentences to extract patterns, and can not extract pairs of terms in different sentences.


Apparently, there seem to be some basic patterns that are finite in number.

there are many variations of these basic patterns.


It is generally assumed that a proposition has a verb which optionally takes a noun occurring before it as the subject and a noun after it as the object.

as mentioned earlier, a cause can also be expressed as a nominalization or nominal.


It can only make very small improvement in the domain of health Third, joint strategy is still effective in dealing with contrast transition.

some results are no better than remove strategy.


Specifically speaking, the advice writers devote themselves to employ the move of advice the most with the supportive move of assessment, followed by explanation and general information.

the authors rarely employed the moves of disclaimer, referral, own experience, and farewell notes.


Distributional approaches are usually applied to linguistic material.

in this study we attempt to use the videos themselves for unraveling some aspects of the mental lexicon.


Mandarin productions include clearly full V1V2 construction but also stative verbs alone (like , broken-away) or more complicated stative construction like (frighteningly into pieces) (as stated above).

since the eliciting question of the protocol was "What did the woman do?


The most straightforward way is to project the video clips in the space defined by verb use (with each verb as one dimension).

this method would have to face the issue that data points would be in two different spaces: Chinese verbs on one hand and French verbs on the other.


Using the rule of Bayes, Equation 1 can be expressed as: Previous works such as that presented in (Stolcke et al., 2000) have proposed similar approaches to DA annotation.

these previous approximations assume the availability of the segmentation of the turn to perform the DA assignation (Stolcke et al., 2000;Webb and Wilks, 2005), when segmentation of the turns is not usual in transcribed dialogues.


On Timebank, AD polynomial composite kernel achieved the best result (i.e., over 6.2% improvement).

on OTC, AAPD gained the best results with 3.45% improvement.


These factors will certainly be a part of the constraints on their mutual interaction.

here the reasons for including emotion and social cognition are twofold.


Many words (e.g., plant) have different senses in different contexts (e.g., green plant and nuclear plant), usually leading to different translations in another language (e.g., 植物 and 工廠 respectively).

different words (e.g., plant and factory) may express very similar meanings (e.g., the working place for industrial labors).


Lesk (1986) is the first to leverage the definitions of words in machine readable dictionaries to predict word senses.

wordNet, a valuable knowledge source encoded with hyponym, hypernym, and synonym semantic relations, is used to measure semantic distances among word senses to help sense disambiguation (Agirre and Rigau, 1996;Galley and McKeown, 2003).


In our experiments, we focused on nominal synsets and hyponym/hypernym semantic relation defined in WordNet.

we looked up 同 義 詞 詞 林 , a Chinese thesaurus for run-time synonym expansion.


Among the four combinations of our system components, HM+FM+TS, a system with hierarchical WTC models, a filtering model, and synonym expansion, achieved the highest Top-1 Hit Rate, 77%, suggesting the WTC models benefited from sense pre-pruning and synonym expanding.

the high MRR (0.84) pointed out that users (e.g., lexicographers) could often find the suitable word sense for the translation by looking at the first two senses in the ranked sense list generated by our model.


For example, in any language, anything that belongs to a category of instruments is assigned the same template.

even with an extended qualia structure, SIMPLE fails to account for the complete range of meaning of possessive construction.


The possessor argument is NP 1 in (I), as in English Naomi's bag whose possessor argument is Naomi.

in (V), the possessor of the bag is NP 2 hito "man" and there is no English equivalent big bag's person.


Chomsky, on the other hand, argues that the derivation is either by the Exceptional Case-marking (ECM) process under S-deletion (Chomsky, 1981) or by IPcomplementation (Chomsky, 1986).

his motivation for not accepting the RTO analyses is mainly theoretical.


Putting all of this evidence together, most transformational approaches claim that Japanese epistemic verb construction involves the RTO movement.

each piece of evidence that they adduce is indirect and shows that the accusative argument may undergo raising from an embedded clause.


The admission of scramblability shown above seems to be problematic for the surfacecompositional analysis of epistemic verb construction in Section 4 since that postulates "control".


6 Kuno argue that no-koto is optionally added to the human object of RTO.

the contrast of the scramblability indicates that these constructions are different and thus we propose that the sentence with the human object could be a different construction, i.e., an epistemic verb construction whose underlying structure is control.


As a consequence, it is often difficult to evaluate individual analyses of wa in terms of their ability of making correct predictions and correct predictions only.

recent years have seen important progresses toward an adequate theory of information structure, where insights from the accumulating works are integrated and problematic conceptual confounds are resolved (e.g.


vP-ellipsis is referred to phenomenon that the vP in the right clause is elided, except an auxiliary verb.

gapping involves the deletion of finite verb, remaining its arguments.


In (28a) and (28b), prepositions that two adjuncts have in common are elided as the gapping schema predicts.

in (28c) and (28d), prepositions are not elided even though they are in the same context.


Even if users find that search results are degraded by unavoidable speech recognition errors, the users have had no means of correcting these errors.

podCastle allows full-text results of speech recognition to be accessed by both users and external search services, and allows a number of users to cooperate with each other to improve the speech recognition performance.


Although speech recognition usually requires a sufficient corpus to provide useful results, such corpora tend to be costly and labor-intensive, thus limiting applications.

this study has aimed at collaborative training for speech recognition where full-text transcriptions containing recognition errors are first disclosed and then corrected by anonymous users.


In western languages, acquisition of nouns precedes acquisition of verbs.


It is rather simple to identify word units in such inflectional languages as English.

the task is not so easy in such agglutinative languages as Korean.


In these examples, [sensayng-nim-tul] and [cap-hi-si-ess-keyss-ta] consist of roots (sensayng and cap-), derivational affixes (-nim and -hi) and inflectional affixes (-tul, -si, -ess, -keyss andta), which can be safely assumed to be word-internal elements.

the elements outside the brackets can hardly be analyzed as either derivational or inflectional affixes.


On the other hand, polysemes have the same etymological source, and, hence, are related semantically.

the distinction is not always clear because the etymologically related meanings of a word can, over time, become so different that the original semantic relation can be obscured.


As the feather is both 'light' and 'not light' at the same time, the word light can be judged to be polysemous.

if a person is a 'child,' it cannot be a 'non-child.'


If we are going to treat the polysemous word newspaper as a single-sense word, it would have to be defined as in (a).

we don't have to use an expression like a male … or a female … in defining child, as we can see in (b).


Two antagonistic units are "mutually exclusive as foci of attention" (p. 112).

there are some sense units which resemble full senses in many respects, but do not show antagonism (p. 115).


Note that when they are attached to host words ending in vowel, the first person =ku becomes morphologically covert and thus represented by the glottal stop (').

the second person =mu can only be reduced to =m but can never be omitted.


Kageyama, 1991; Lieber, 1992; Matsumoto, 1996) which claim that complex predicates are necessarily formed in syntax.

benglish verbs like /in kOra/ or /kOnfuz kOra/ are problematic for these approaches because it is unclear how preposition in or flexional verb confuse can appear as the arguments of the verb /kOra/ 'to do' in an underlying syntactic structure.


The predicates like /kOra/ 'to do' and /newa/ 'to take' can link their respective arguments in (3-6).

it is unclear how ALP can be satisfied in (9-11) because categories like flexional 1 Singh and Backus (2000:83) put bilinguals into two different groups: i) Perfect or true bilinguals and ii) less than perfect bilinguals.


Using Russian adaptation of the working memory span test from Daneman and Carpenter 1980 we demonstrated that a rhetorical distance is really a significant factor affecting referent activation as well as that working memory capacity is correlated with some referential processes.

we did not find an interaction between rhetorical distance and working memory group.


With respect to the individual-differences approach, the Single Resource Theory (SR) predicts that having a low WM capacity will reduce the resources available for sentence processing and make it less efficient.

the part of the verbal WM system is specialized for interpretive aspects of sentence comprehension (Caplan and Waters 1999).


They concluded that in the task assigning the structure of an object-relativized sentence such as (1) is more demanding than a participant-relativized sentence such as (2).

in order to argue for the SR theory it is necessary to find an interaction between sentence complexity (high, low) and WM group (high, medium and low span).


King and Just (1991) provided evidence that there is a main effect of span, a main effect of extraction type, and a main effect of the interaction between syntactic complexity and span.

caplan and Waters (1999) noted that the required statistics were not reported.


We follow the Phase Theory view that a derivation proceeds in phases, and that phases are sent to Spell-Out as chunks.

we propose a modified form of the PIC (see section 1) in which a phase is sent to Spell-Out as a complete unit.


In ME model, appropriate feature selection is a very crucial problem and also a key issue to improve classifier's performance.

it does not provide any method for automatic feature selection and heuristics are usually used for this task.


domain dependent knowledge and/or resources.

our proposed algorithm is based on a relatively small set of features that can be easily obtained for almost all the languages, does not make use of any domain dependent information, and thus can be replicated for any resource-poor language very easily.


Each function represents a paradigm of some part of speech.

it is the lexicon that connects the lemma to its paradigm.


In concrete syntax, we define a category N as a record having a table "s" from number to case to string (Number => Case => Str) and an inherent parameter "g" of type gender, as shown below 7 : lincat N = {s:Number=>Case=>Str; g:Gender} ; param Number = Sg | Pl; Case = Dir | Obl | Voc | Abl ; Gender = Masc | Fem ; Nouns can be divided into different groups based on their inflection which are mentioned by Bhatia (1993:164-166) and Shackle (2003:600-601).

we have made suitable changes with respect to morphological context wherever needed.


To extract multi-token words, a list of common prefixes and suffixes could be used.

we have not done it for the work reported here.


So there is great potential that the terms having sentiment polarity are not the attitude of the author in the sentences including Interrogative suffixes.

there is still potential that the terms in the influential range of interrogative suffixes express the opinion of the author as in 난 왜 이 영화가 재미있을까?


This means that various linguistic features used in this work properly grasp the sentimental meanings included in subjective texts.

the subtopic news article corpus shows the smallest improvements.


The first thing to note concerns the optionality of determinants.

their optionality has often led literature to assume that the Korean NP structure is non-configurational or lacks the category D. 4 as we have noted, the absence of the determiner does not mean its non-existence in the interpretation of NP structures: context provides its deterministic meaning.


Because onset and coda must be a consonant, the AVMs for ONSET and CODA contain const-feat.

since nucleus must contain a vowel, the AVM for NUCLEUS contains vowel-feat.


A number of previous studies have suggested that case markers in Japanese and Korean cannot be dropped when the argument they mark is contrastively focused (Tsutsui, 1984;Masunaga, 1988;Yatabe, 1999;Ko 2000; D. Lee, 2002).

these studies were not based on careful comparison of patterns of case ellipsis when subject and object are in focus.


B-2: ani, i chinkwu kochi-ss-e. 1 2 3 4 5 no, this guy(-Nom) fix-Pst-Ind 'No, this guy fixed (it).'

in the case of the informational focus condition, neither the actual element in focus nor the potential alternative(s) are explicitly given in the context.


Thus, data elements in a repository must have clear access policies and clear addressing.

making access completely wide-open for valuable date is also undesirable, as there are situations when access must be restricted to certain parties.


As argued in the previous section, we assume that the thematic topic WA and GA for neutral descriptions do not induce the application of Existential Introduction to generate alternative sets (or which are taken to be invisible because they are irrelevant for truth-conditional meanings of sentences).

the focalized subject marked with the exaustive listing GA elicits a set of alternatives (or make it relevant and visible for the interpretation).


Therefore the target word is usually recognized by a LVCSR.

the accuracy of the LVCSR is generally insufficient.


In our method, if an input is an order utterance, the edit distance between the outputs from a DSSR and the LVCSR becomes small.

if the input is not an order utterance, that between the outputs from each DSSR and the LVCSR becomes large.


The LVCSR is used for utterance verification, namely output selection, and capturing the context information in a dialogue.


The method needs to recalculate a model to select an output.


7 The fact that sentence 7, which is a monoclausal sentence containing the causative and resultative verbs, is acceptable in Thai suggests that it does not exhibit redundancy.

sentence (8) is marginal and even unacceptable for some native speakers because it apparently exhibits contradiction between the first and the second conjunct clauses.


Pederson (2007) argues that the whole-for-part metonymy is a strategy which allows Tamil speakers to use a transitive verb such as the verb for 'break' to refer only to the first part(s) of the event.

they cannot use this strategy when they use the converb construction, which entails realization, otherwise it would be contradictory.


The rejection arises from the fact that the resulting state of dying is lexically entailed in kill.

shā in Chinese falls in the implied-result verb category because it accepts both patterns (A) and (B).


This progressive marker normally appears with activity verbs, such as John is running, John is reading a book.

stative verbs can occur in this construction in marked contexts.


So, our method is also based on a graph.

we use syntactic analysis technology to get co-occurred word pairs.


Statistics based techniques can extract common used terms with statistical significance.

the techniques are very sensitive to term frequency, and thus terms with low frequencies cannot be extracted.


Quantifiers for which such elimination is possible are sortally reducible.

quantifiers for which such elimination is not possible (like M OST for instance) are inherently sortal.


It was found that if two terms A and B are in this relation then they occur in a context like A is a (kind of) B, so a search for such pattern may yield a list of possible hyponyms.

to statistical methods, lexical patterns approach allows to discover hyponym (or hypernym) of a given term from single occurrence in a corpus.


According to them, the best analysis is the one which is the most proximate to psychological reality.

it is difficult to define psychological reality/validity under their framework.


The goal of lemmatization is to reduce the impact of inflectional morphology of words on statistical analyses.

the goal of PoS tagging is to provide a way to group/classify the context/usage of a collocation/phrase.


We have implemented our own EBMT system using proportional analogy and have found that the analogy-based system on its own has low precision but a high recall due to the fact that a large number of names are untransliterated with the approach.

mitigating problems in analogy-based EBMT with SMT and vice-versa have shown considerable improvement over the individual approach.


Hereafter all the improvements essentially denote relative improvements.

a combination of AEBMT with the SMT (SMT+AEBMT) shows results of 31.8%, 37.7% and 29.6% accuracy with the improvement of 0%, 4.1% and 240.2% respectively for three models.


The claim might be insignificant when transliterating NEs as a task on its own, as other models (character and syllable level) have higher accuracy.

in the case of full text translation, SMT models are trained at the word/phrase level so can only transliterate names that are seen in the corpus.


A similar effect has been observed in case of our word-level NE transliteration experiment.

our AEBMT models inherently consider every word/sentence as a string of characters.


We have found that the analogy-based approach has less recall (unable to find any solution in many cases) compared to SMT.

a combination of both has achieved a reasonably good improvement over the individual model.


Some early experiments (Clark and Clark 1968;Smith and McMahon 1970) have really shown that on a sentence verbatim recall task normal adults made significantly fewer errors remembering the sense of 'Main-Sub' sentences, as in (1b, d), than of ' as in (1a,c).

studies of normal adults (Smith and McMahon 1970), kindergarten-and school-age children (Clark 1971;Natsopoulos and Abadzi 1986), and aphasics with relatively preserved auditory comprehension (Ansell and Flowers 1982) indicated that on a sentence comprehension task clause order did not significantly affect the comprehension accuracy.


Our two-factor hypothesis confirmed as well: 'Before2' was the hardiest type and 'After2' was the easiest one.

we need further empirical justification of the fact on the experimental materials of our main experiment.


We need to divide its correct senses and assign its appropriate senses to the different contexts.

"lexical ambiguity" is presented in several different related researches to present and refer the same target.


In CWN, there are 28 senses for chi1 "eat", 9 senses for wan2 "play", 5 senses for huan4 "change", and 14 senses for shao1 "burn", and in Xian Han, there are 8 senses for chi1 "eat", 3 senses for wan2 "play", 3 senses for huan4 "change", and 8 senses for shao1 "burn".

we only focus on the transitive verbs in this sense prediction; we need to remove the noun usage sense and non-transitive verb usage senses in CWN and Xian Han.


The transliteration system used in this paper is the most internationally known Yale Romanization System.

data in KOLON is transliterated via the "한글 의 로마자표기 방법 HANKUL-UY LOMACA PHYOKI PANGPEP ("Rules of Writing Hangul in Roman Letters")", also called "(Ministry of Culture) Revised Romanization (2000)", the most wide-spread system in South Korea nowadays.


Except for the last column, which we needed to add to the structure as explained above, the format follows exactly the one found in µK (Table 2).

for the lexical frames to be able to hold the information contained in Sejong, we had to create some new slots (FRAME, FRAME-TYPE, MORPHOLOGICAL-STRUCTURE, SEMANTIC-CLASS, TRANSLATION, EXAMPLE) and a new facet, namely, JOSA (from the Korean 조사(助詞) COSA ("particle")).


In terms of computational implementation, there still are more issues for our analysis to be resolved.

we can observe that the grammar implemented in the LKB system appears to be feasible enough to extend to more complex data in a process of building a comprehensive KRG.


those of activities (search) and achievements (find).

the counterparts of 'chacta' in Japanese, like English, are distinguished as sagasu (search) and sagasisasu or mitukeru (find).


'-ess 1 -' functions either as the perfect aspect marker or the past tense marker according to the aspectual classes of the verbs that they combine.

'-ess 2 -' functions only as the past tense marker.


The verbs combined with '-ess-' refer to either the occurrence of the punctual situation at the moment of speaking or the present resultative following the termination of the situation.

those combined with '-essess-' refer to either the past completion of the punctual situation or the past resultative state as shown in the following examples .


Such ordering can explain scope reversals of universals vis-à-vis existentials, for example.

aoun and Li (1993) have provided an account of scopal interpretation across languages like Chinese, English in terms of two principles: Minimal Binding Requirement (which stipulates that variables must be bound by their most local a-bar binder) and Scope Principle (which states that a quantifier a may have scope over a quantifier B iff a c-commands a member of the chain containing B).


Since a proposition is a set of possible worlds which is of type <s, t>, the set of possible answers is a set of sets of possible worlds, namely, type <st, t> .

syntactically speaking, sentence-final particles are functions from a sentence to a question or a focused sentence.


Benefactive predicates, on the other hand, are seemingly composed of an infinitive (V1) followed by te which is arguably a marker, and an auxiliary verb (V2).

the syntax of the infinitive complementation, where the V1 in the sentence final verb cluster is semantically a complement of V2, has long been a issue in Japanese generative grammar.


A D-tree shows a relational characteristic of the syntactic representation in the form of hierarchical links between items, i.e., which items are related to which other items and in which way.

a phrase-structure grammar uses a phrase-structure tree (PS-tree) to describe the groupings of words into the so-called constituents at different levels of sentence construction.


A D-tree contains only terminal nodes; no abstract representation of syntactic groupings is used.

a PS-tree contains both terminal and non-terminal nodes; most nodes are, however, non-terminals.


Syntactic class membership is a way of labeling syntactic roles in a PS-tree because a PS-tree does not and cannot specify the types of syntactic links existing between two items in a natural and explicit way.

to PS-tree, class membership is not specified in a D-tree.


This considered linguistic resource can be used as parallel or comparable corpora: it can be considered as gigantesque lexical resource, available freely for all users, for many domains and diverse languages.

its exploitation in NLP research is recent, not completely pertinent and still requires theoretical ideas and practice on its statute, characteristics and limits (Adafre et al., 2006;Schönhofen et al., 2007;Erdmann et al., 2008a;Erdmann et al., 2008b;Erdmann et al., 2009;Adar et al., 2009;Mohammadi et QasemAgharee, 2009 ;Yu et Tsujii, 2009a;Yu et Tsujii, 2009b).


The technology of web search has provided a powerful platform for seeking and accessing information on the Web.

due to the limitations of keyword based retrieval, people often find it hard to locate their desired content through ad hoc searching.


They are expected to vote based on their conviction about the quality of the answer.

the results of voting would not be revealed to the community until the voting period is over.


While there are some exceptions to Talmy's (2000 a&b) typology, most languages have tendencies to generally fall into one category or the other.

slobin (2004) has since elicited the need to add a third category, which he calls equipollently-framed languages, to Talmy's original typology.


Some examples, taken from Levin (1993), are shown below.

advance, arrive, ascend, come, depart, descend, enter, escape, exit, fall, flee, leave, plunge, recede, return, rise, tumble many of the above verbs have been borrowed into English from verb-framed languages.


When the English learners were divided by length of stay, as opposed to by TOEFL score, the amount of difference between the learners' tendency to use satellite framing became slightly larger, but not significantly so.

the difference in how manner was encoded became much more significant.


The experiment results show that CRF model can achieve good precision in term recognition if trained with known term list.

with regard to discovering potential novel terms for terminology lexicon editors, CRF model fails to show good performance, if trained with known term list only to predict novel terms in testing corpus.


For the average performance of these 10 subsets (see Table 6), the highest recall among these 10 subsets only reaches 0.767 and the highest F-score is 0.828.

we can see precisions across the 10 subsets are around 0.901 on average, which is a satisfying result of applying CRF model into recognizing novel terms, especially considering that only syntactic knowledge is used in this experiment.


As a result, this omission increases the number homographs (words with the same writing form).

arab readers normally differentiate between homographs by the context of the script.


Also, it shows the ratio between unique sub-tokens in Arabic and main token in English with an average ratio of 0.852.

arabic has more morphological inflection than English.


In case of the rare language-combination: Danish and Japanese, it is possible to collect a reasonably sufficient amount of corpora between Danish and English as well as English and Japanese.

the availability of bilingual corpora that directly link Danish and Japanese are very limited.


The "word-association" model includes a direct connection between the lexical representations of the two languages, while only the SL lexical representation is connected to the layer of conceptual representations (Figure 1-a).

in the "concept-mediation" model ( Figure 1-b), the lexical representations of the two languages are connected through the layer of conceptual representation that is shared between them.


Actually, in (Ishikawa et al., 2010), we developed learning examples by using target user's messages submitted to the target category.

the scope of target users was limited because there is a limited number of users who submitted more messages to the target category than the minimum needed to develop their learning examples.


chiebukuro, the target users were only 312 users when we developed training data (300 answers) and examination data (50 answers) only by using answers submitted to the target category.

there were another 808 users when we relaxed the criteria of selecting learning examples and used answers submitted to non-target categories.


We consider that these are few, because these were made from approximately 200,000 nasty comments.

when the threshold value is low such as 0.1, the F-measure is high.


Results show that accuracy does not have the huge difference between the proposed method and comparative method.

different type of comments were detected.


Notational variants for a word appear in a similar context.

we might extract wrong notational variants when only using the condition of a similar context.


or "He is putting on a hat" -a iss-has been found to be the resultative marker in Korean (Martin, 1992).

whereasko iss-is compatible with transitive verbs, -a iss-can co-occur only with intransitive verbs in describing a persisting state resulting from a completed action, as in (3).


This action-inprogress meaning is obtained when the progressive marker is attached to activity verbs and accomplishment verbs.

progressive meaning with accomplishment verbs has been shown to be slower in development than with activity verbs, at least in L1 acquisition of English (Shirai 1991).


As Table 5 shows, target aspect marking was the most frequent in the progressive -ko iss-: 56.2%.

the differences in frequencies of use between the two resultatives were small, 40.2% versus 41.4%.


They suggested that these extracted sentences could be used as summaries.

a review basically consists of sentiments with various aspects (i.e., "image quality" and "usability" of a camera).


These similar opinions have possibilities to be redundantly extracted as a summary.

the opinion mentioned by many reviewers is important.


In our subjective evaluation, we verified that the summarization by using the three features was effective.

in quantitative evaluation, there was no great difference between the summary using all three features and summaries using only one or two features.


Another problem is that it cannot handle summarization of the information that is not described in the definition sentences of a word dictionary.

we can handle parts of speech other than verbs by using co-occurring words.


(Zhung, 2002) classified the meaning extension of "kàn" into physical, mingle and mental categories; and all of which are derived out of the interaction of metaphor and metonymy.

no formal theory was used in these two studies to test the compositional mechanism of Chinese data.


Thus, the English CG we were using for our annotation task, though in principle designed for written text of the news and scholarly genre, can also produce annotations for data of varying degrees of what we will call orality 1 in this paper -text with a certain amount of features typical of spoken language.

even a robust written-language parser will obviously be liable to a higher error rate when confronted with spoken-language structure and category distribution, and we therefore adapted the parser on several points.


For example, given T = is but a dream within a dream and POS-annotations V, Conj etc., the annotation layer might look like this: Although in this example the annotations each relate to one word of the text, they could as well start and end in the middle of words or span across several words.

i do, at this point, assume that annotations are non-overlapping.


(1) " ([((word="ekki" %c)  Most of the dialects in the corpus have been transcribed with one transcription only; that of the standard orthography of that country.

the dialects of Norway and of the two areas of Älvdalen and Gotland (considered to have dialects very far from Standard Swedish) have also been transcribed with a script that is phonetic-like (the Norwegian version is compliant with the transcription used in Papazian and Helleland (2005), the Övdalian one is the one recommended by the Övdalian Language Council, while the Gotland one has been used by the Swedia 2000 project).


In recent years, corpora have gained a strong foothold in learning and teaching.

a major problem, highlighted by language learners and tutors alike, is that students often find it hard to understand the language written in the concordance lines; most corpora were not built with the language learner in mind and are made up of authentic texts of various genres and therefore no attention has gone into selecting texts of an "appropriate" level.


Comparison of groups (A) and (B) in Table 2 suggests that native speakers of English use more "rough" patterns (31.0%) than English learners do (17.8%) in (B) (z = 5.03, p < .01).

it is not the case in the Japanese data.


Introspectively speaking, event reference functions to fill (nominal) entity-coherence gaps and appears in NULL-labeled environments.

it does not seem to be the case; the distribution is noticeably varied among the four types of TRANSITION types.


In addition, the demonstrative pronoun this in a subject position often co-occurs with emotion verbs, such as upset, worry and cheer.


The word pairs extracted from the files of the fifteen other movies have been annotated by only one student and have not been verified.

the verification and cross checking performed on the two special movies enables us to relatively trust the quality of the unverified translations.


For the movie graphs, we have modelled that a Mandarin verb is any V 1, V 2 or V 1 − V 2 that is the translation of a French verb.

we realised that many occurrences of French speakers did not belong as such to the lexicon formalised by DicoSyn.Verbs, despite being translatable by a Mandarin verb.


In addition, the distance among these G verbs are between 0.4 and 0.6.

the nearest words of the S verb qia (掐/pinch) include S verbs and G verbs with distance over 0.8 (see Figure 2).


It is reported that the small p-values (9.779e-07, 1.324e-09, and 1.191e-13) in the first three sets of data (carrot-peel (f 6), paper-crumple (f 2), and plank-saw (f 16)) suggest a non-proportionality of S verb in different age groups.

the p-value (0.8467) obtained in the last data set (glass-break (f 3)) is too large to suggest a significant variation of S verb proportion in different age groups.


The κ-score ranges from 0.35 to 0.59 which is far from perfect.

this was partly explained by the level in French among the students who was not the same and they got a different idea of not being too strict in the alignment.


We therefore went back in the original French transcripts and find out that in fact in some cases, relevant constructions quite comparable with Mandarin construction had been discarded when selecting the verbal form (3).

(3) a. elle a découpé le pain en deux morceaux (she cut the bread into two pieces) ; découper b. elle a partagé en deux une flute de pain (she cut into two a bread) ; partager c. elle coupe en morceaux les brocolis (she cut into pieces the broccolis) ; couper the Taiwanese data featured a lot of hapaxes like (2) that we removed for performing a second alignment.


In particular, it seems difficult to provide a definition for such an individual or its sort.

we have a clear concept of an entity which is "a physical object such that contains information" and conversely "an informational object such that is contained in a physical object".


Even though walls are not (proto)typical objects that can be read, the phrase can be understood without too much effort.

phrases such as read the bones or read the bowels would require a clear context of shamanism and metaphoric extension, before they could be interpreted, i.e.


The result shows: 1 Further pairwise comparisons suggest that for five-year-olds, children"s overall performances (i.e., scores of I+A+O) of the four verb types (i.e., Specified, Open, Control, Filler) are all significantly different from each verb types, with Control scores the highest, Filler the second, Open the third, and Specified the last (i.e., C > F > O > S).

for seven-yearolds, children"s overall performances of Specified and Open verbs are significantly different from the other three verb types, but Control and Filler verbs are not significantly different from each other.


Further pairwise comparisons reveal that the outcomes of Control verbs are the easiest to achieve, and the second easy outcomes are Filler verbs", the third Open verbs", and the least easy-attained outcomes are those of Specified verbs.

the outcomes of Open and Filler verbs are not significantly different in their easiness of achieving.


The result of the production (paraphrase) task suggests that not all of them can properly paraphrase the novel instrument extensions of familiar verbs.

paraphrase of Open verbs are achieved earlier (in age five) than that of Specified verbs (in age seven).


In their study, they included non-verbal response such as pantomimes to be correct responses of paraphrase.

in our study, production data were coded according to correct verbal responses of verb and instrument.


"s chosen verbs are verbs which take inanimate objects as default instrument and can also used as nouns such as vacuum or fork.

in this study, the default instrument of each target verb is "hand", the most dominant body-part instrument in acquisition (Maouene et al., 2006), and the verbs cannot be used as nouns at the same time.


A longer exposure may result in a different pattern of extensibility.

the discrepancy between the label of the object (e.g.


Type D: puthi-'attach, paste', palu-'paste', chilha-'paint', sekk-'mix', ... As witnessed in case alternations in transitive motion verbs, a Goal expression changes to a derived Theme (C. Lee 1997)('globally/fully affected') as Incremental one (Dowty 1991) in Quantization and tends to exhibit change of state, as in (17), (19) and (21) below in English.

alternation patterns in English and in K/J are different from each other.


We may be tempted to make use of the underspecification of headedness, showing the 'globally affected' change of state, which is not entailed but conventionally implicated.

the denotation of (e2) in (26a) is satisfied by the Figure's contact/central coincidence with the Ground, whereas the interpretation of (26b) requires the derived Theme's being 'globally affected.'


Cross-Language Information Retrieval (CLIR) system uses dictionaries for information retrieval.

out of vocabulary (OOV) terms cannot be found in dictionaries.


The quality of the translation is incomparable between human translation and automatic translation.

automatic translation offers the speed and low cost over human translation.


While the rendition of a name in the target language is expected to phonemically resemble its pronunciation in the source language, difference in phonotactics between the two languages would be compromised during transliteration, and incompatible phonological structures might result in extra syllables in the transliterated name.

name transliteration tends to be more conventional than innovative, such that the same chunk is often transliterated in the same way even when embedded in different names.


On the one hand, Forward Maximum Matching apparently performs slightly better than Backward Maximum Matching.

many of the low frequency segment pairs of various syllable lengths are in fact very useful in transliteration, as is evident from the drop in performance when the lexicon was reduced in the second and third trials.


One possible objection to this test would be that a grammar without the packing of argument structure information could be implemented in a different way, that would make parsing more efficient.

this comparison is only done for testing the impact of the packing of argument structure information in a grammar that is implemented similar to Norsyg.


One of the possibilities for making (13) intelligible is to yield the reading "a police officer caught a criminal and there is a man."

to get such an interpretation, man(r) should be associated with an event argument, and the discourse referent r must be linked to a theme role, as in theme(r, e'), where e' is an event argument different from e. In other words, man(r) itself is a well-formed condition, but without an event argument it is not a well-formed event-semantic proposition.


Given the usual translation of a universal quantifier λQλP∀x[P(x) → Q(x)], in which the antecedent and the consequent clauses share the same variable x, it seems reasonable to assume that the two subordinate boxes in (19) share the same variable in their conditions when a universal quantifier is translated into its box notation.


Unlike the case of the sentence (9) discussed in Section 4.1, the u associated with keekan 'police officer' is not in the right box, so that it is not the primary option for the replacement of x.

the r introduced by otoko 'man' is in the same box as the agent variable is, so the locality preference in (11) chooses r as the agent of the verb.


The effects of these strategies are similar to the effect of the minimal attachment (Frazier and Fodor, 1978).

a syntactic approach with the minimal attachment cannot account for the fact that the universal quantifier reduces the GP effect.


A prepositional phrase is specified in the target attribute of a character goal.

the target attribute is also used to specify the object of the verb, in which case a PangngalanSpec is instantiated for the noun phrase.


Current results show that the method works better for concrete senses, and is in many cases able to make a dichotomous distinction between concrete and abstract concepts.

it is possible that the different defining styles may reveal how describable a concept is, amongst the many factors influencing one's perception of concreteness, and it happens that some abstract senses might be more describable than others.


The data shows that zo3-ded4 (做得) is the dominating one to express possibility or permission in deontic modality; the adoption of zo3-m5-ded4 (做毋得) is preferred than that of sii2-m5-ded4 (使毋得) in expressing prohibition.

the role to denote the idea of "not necessarily" falls on m5-sii2 (毋使) exclusively.


9 One specific「NEG+M+NEG+VP」 construction is also observed here, which denotes the idea of "¬◊¬p ≡ □p" as well.

while the [不得不-V] construction in Mandarin Chinese implies unwillingness of the speaker, the [做毋得毋-V] construction does not.


In other words, the members of zo3 (做) types are in predominance.

the application of sii2-ded4 (使得) and sii2-m5-ded4 (使毋得) are fewer in linguistic use.


Subsequently, WS systems that perform strongly in terms of F-score have been considered reliable and are selected as the preprocessors for the commonly accepted notion that WS accuracy is directly related to the effectiveness of follow-up applications.

this perspective has sometimes failed to win significant support, particularly in the Chinese IR area, because of the absence of strong evidence confirming the causation.


Kwok, sees more accurate segmentation as generally improving retrieval when content-bearing terms are involved, though exceptional cases exist that may average out the results, particularly for long queries where sufficient redundancies exist to remedy incorrect segmentations of short queries.

the definitions for term types such as content-bearing and query lengths are unclear.


So the feedbacks are normally used to adaptively learn user intention and iteratively improve the retrieval performance.

as the discussion above, the judgment on the satisfaction might be imprecise.


the main characteristic of SNF is that the results conflict with the truth.


Besides, by inspecting the recalls on different patience levels (see Table 3), we find two interesting phenomenon:  Users should have little patience to check more feedbacks (see the column of "Clicks" which gives the number of clicks on different patience levels)  As the patience level increases, the recall of factual feedback rises (see Figure 2) The phenomenon illustrate that users can gradually raise the capability of recognizing the factual feedbacks if they have the patience to check and learn more information.

the truth is that most users only check 1 to 3 feedbacks (see Table 3), thus the probability of reaching factual feedbacks and successfully recognizing them are both low, especially when the factual feedbacks are far from the top.


Thus, considering the little patience of users and the disturbance of over-learning, it is highly necessary for search engines to top-rank the factual information, otherwise users either miss the factual information or lose themselves in the redundant information.

as reported in 1), actually only 50.09 percents of factual information appear in the top 10.


It is because they can completely demolish their prior opinions after go deep into the details of information.

the condition of factualness is very different.


The rising trend of the satisfactoriness reflects the possibility that users can be easily satisfied so long as the information looks interesting, relevant or/and suiting their tastes.

the reducing trend of the factualness illustrates that users may be more fastidious about the factual information.


Thus it seems users have completely different cognitions on the satisfactoriness and factualness.

by analyzing the differences between their SAC, we surprisingly found the cognitions are very similar (see Figure 7): both the SAC trends reduce as click number increases.


This type of approaches considers the quality difference between the sentence pairs in the corpus.

it still views the sentence pairs as independent.


The results obtained by Method 2 and Method 3 are similar, since both of them consider the QI when given the set of selected sentence pairs S only.

when comparing the Method 2 and 3 with Method 4, we find Method 2 and 3 obtained better results when selecting only 10% and 30% of the corpus, and after increasing the ratios, method 4 obtains the better results, especially it obtains the best results when selecting only 80% of the corpus.


In examples (a) and (b) prepositions "off" and "away" can appear before as well as after the object (light in example (a) and plates in example (b)).

in examples (c) and (d) below the preposition "out" can appear only before the object (change and solution, respectively) and not after it.


Previous research on VPCs has focused on their automatic extraction and classification (Baldwin and Villavicencio, 2002;Villavicencio, 2003).

systematic study about how they should be handled during translation for a particular language is noticeably lacking.


The structure of all the translated sentences is also similar (except (f) which is a translation divergence (Gupta and Chatterjee, 2003)).

the Hindi verbs being used in the translations are different and depend on the object as well as its semantics used.


These VPCs are entered into the lexicon with their meaning.

one needs to make sure in what situation they are acting as a VPC.


We expect that similar techniques will prove to be helpful for other target languages by focusing on the lexicon and the verbs of that particular language.

there are sentences, such as, "the thief broke into the house" in which we do not have a related verb for the NP ("the house").


This confirms the results given in (Lardilleux et al., 2009) that the sampling-based approach excels in aligning unigrams, which makes it better at multilingual lexicon induction than, e.g., MGIZA++.

its phrase tables do not reach the performance of symmetric alignments from MGIZA++ on translation tasks.


Finally, new kinds of data have to be created in the perspective of validating our model against spoken language corpora.

we propose in this section a preliminary study on real data, giving some indications on the efficiency of the model.


It is interesting to observe that though the incidence of consonant final neuter nouns in the overall distribution of nouns is considerable (19.24 %), children are not able to select the correct plural affix (20%).

the performance of adults for the same class of nouns is encouraging (80%).


In their study on the L1 acquisition of Korean, Seda and Lee (2002) found that both Korean and Turkish children use past marking predominantly with accomplishment and achievement verbs first, supporting the Aspect Hypothesis.

they only focused on the acquisition of past tense .


-ko iss-has been generally treated as a progressive marker similar to the English progressive marker be -ing, as shown in (1a).

it is not obligatory to employ the Korean -ko iss-to describe an ongoing event, unlike with be -ing in English and -te i-in Japanese.


-a iss-has been defined as the resultative marker in Korean (Ahn, 1995;Lee, 1993;Martin, 1992).

whereas -ko iss-is compatible with all transitive verbs, -a iss-can only be used with intransitive verbs in describing a persisting state resulting from a completed action, as in (3) (Lee, 1991).


Their results suggest that the L1 acquisition of past markers shows similarity cross-linguistically.

there has been no study focused on the development of Korean imperfective aspect markers.


This has been confirmed in research on Indo-European languages.

shirai and suzuki (in print) reported that in Japanese, the progressive marking of -te i-emerges with achievement verbs rather than with activity verbs.


JONG even uses the resultative -ko iss-/-a iss-predominantly more than the progressive -ko iss-.

for over 1 year AN uses only the progressive -ko iss-from the first emergence of his use of imperfective markers.


His caretaker"s also shows a very high percentage of progressive marker usage compared to the other caretakers (69% vs. 51% and 49%).

in the English data, the initial restriction of the past tense form to achievements is almost absolute (95% or higher), whereas in the mothers" speech the association is about 60% (Shirai and Andersen, 1995).


According to the error analysis, system developers improve the translation rules/dictionaries of rule-based MT systems or add bilingual corpora/dictionaries to statistical MT systems.

all the evaluation methods mentioned that include human subjective evaluation provide less information for error analysis because they give only one score for each document or sentence.


As mentioned in section 2.2, the objectivity of evaluations could be increased through using questions that check whether certain words or phrases are included in the translated text.

if compatible words (synonyms) are not included in the test set, the evaluators judge the text by their own standards.


19 ("了" ("la") and "着" ("zhe" in Chinese) according to "--てい る"("--teiru" in Japanese)), the scores were exceptionally below the average for the six systems.

the scores for systems E and F were relatively bad, but for items No.


The hierarchical phrase-based (HPB) model on the basis of a synchronous context-free grammar (SCFG) is prominent in solving global reorderings.

the HPB model is inadequate to supervise the reordering process so that sometimes positions of different lexicons are switched due to the incorrect SCFG rules.


The hierarchical phrasebased (HPB) translation model (Chiang, 2005), which adopts a synchronous context-free grammar (SCFG), is considered to be prominent in capturing global reorderings.

the HPB model is weak in controlling the reordering process.


The order of non-terminals should depend on their concrete translations and contexts.

the decoder does not take into account those factors when reorderings happen.


Recall the example of Figure 1, "anshui" and " " are ambiguous words for the rewriting method, since both "anshui " and " anshui" are reasonable phrases that should be translated without reordering.

if a rule reorders "anshui" and " " according to the word alignment, it is probably incorrect and should not be used here.


And those pv collocations are prevalent in the training set.

the result turned out a decline on BLEU score.


At the disambiguational level, early work by Chanod & Tapanainen (1995) showed that even a small set of CG rules can compete with an off-the-shelve statistical tagger.

in their experiment, both systems had access to an extensive lexicon an a mature morphological analyzer (a finite-state transducer).


On the one hand, corpora can be used, leading to an emphasis on tokens and true frequency.

a dictionary can be used, with a type focus, and "frequency" meaning that there are many words in a language with a given ending.


in the original formalism, these tag-adding rules could not make reference to word form string elements, and could not be used to add entire reading lines to unknown tokens.


Spring cleaning is intended to be used in the course of grammar development and as such must leave the grammar in a state that is still easy to maintain.

the second technique, 'grammar compression', computes the smallest subset of a type hierarchy that can assign the same structures to the same sentences as the original grammar.


The dictionary is a very important lexical resource in any field of studies.

wordNet, originally created by academics at Princeton University, is just as important if not greater (Fellbaum, 1998).


When translated in Bahasa (both Malay and Indonesian), a worm is cacing.

once the basic word divides in subordinate categories, the two Bahasa languages also divide.


In this case we should not necessarily believe that w differs in meaning between A and B because, based on w's distribution in B, we expect its computed meaning to vary.

if the computed difference in meaning of w between two random samples from B were small, then the observed computed difference for w between A and B might be more indicative of w having different senses in A and B.


The phonological changes that occur as a result of the association of the verb stem and a suffix can be explained using the rules given above.

there are cases when stems undergo some modifications that cannot be explained phonologically as in suppletive or irregular forms.


For example: "looka fter" should be "look after", and the LMS will give suggestions for each word.

these words should be resolved together.


The time complexity of this measure would appear to be linear with N , the size of the vocabulary.

the storage vectors hold a maximum of D SV 2 (T CF) pairs, where D SV is the dimensionality of the storage vector.


The most popular similarity measure in literature is cosine measure which calculates similarity score though common words between two sentences.

we notice that ambiguous sentence usually have fewer words than document, and a lot of topic related sentences can barely share same word except some empty words.


1, Tagalog NP complements take flexible position in the sentence.

nominal and adverbial elements may take pre-verbal position (Kroeger, 1993) such as the sentences in Fig.


In case of a verbal predicate, the head of the VP phrase is the head of the sentence.

section 3 explains different positions of verb in a sentence.


This may imply that correct POS information have helped choose the head of the word and the use of other morphological information have harmed the performance of the parser.

the auto-tagged POS data in Table 6 shows different results.


(2008) present a method for generating summaries of different length, and demonstrate a correlation between the level of user interest in a topic area, and the preferred summary length.

their method relies on hand-compiled summaries of expanding length, and no attempt was made to automate the summary generation.


Several approaches for this step have been proposed such as the longest common substring (Agichtein et al., 2001), substrings in suffix trees (Ravichandran and Hovy, 2002;Ruiz-Casado et al., 2007) and edit distance based alignment (Ruiz-Casado et al., 2007).

a portion of the retrieved sentences can be, to a certain degree, not relevant in describing the intended relation.


For example, in the Chinese portion of Wikipedia, the birth place of Kobe Bryant extracted from infoboxes is "賓夕法尼亞州 (Pennsylvania)" and "費城 (Philadelphia)".

there are several translations other than the above for "Pennsylvania" and "Philadelphia" in Chinese, which all could appear in the retrieval.


When generating patterns, S ⟨H,T ⟩ \ S O will cause relevance problems, which makes the pattern induction procedure producing deviated patterns.

s O \ s ⟨H,T ⟩ will cause specificity problems, which undermines useful patterns in evaluation.


In Table 1, we can find although the relevant ratio is quite low in the Death Place relationship, which affects the performance of the pattern-based systems.

on this relationship, our approach achieves a higher precision score (0.67) than the compared approaches.


In the Birth Place relationship, the F E, CE and Espresso approaches would get a lower precision at top 500 ranked patterns.

our approach achieves a perfect precision score of 1 on the top 500 and 1000 patterns.


Our approach achieves the highest precision score on the top 500 ranked patterns, but the score decreases slightly as N increases.

the Espresso's precision score increases with N ranked patterns.


It was assumed that the answer given to the models has no effect on the system performance.

in Chinese, some research on NER (Mao et al., 2008;Yu et al., 2008), the work of Yu et al.


The performances of the systems with answer pattern 2 to 5 are almost indifferent, the difference of the values of f-measure is less than 1%.

when compared with pattern 1, their performances are about 3% better.


Since the named entities are divided into 5 categories, the system, therefore, needs a large number of examples of names in each category for training.

from all of the entity types, the organization names referring to the locations have the lowest number of examples (147 instances), while the highest number of entity names is 5,672 instances which are person names.


For instance, if the location names are followed by verbs or the organization names are preceded by prepositions, they tend to be metonymic names rather than common names.

in this study, we did not use the deep linguistic features like POS tagging, so the systems could not know the part of speech of each word.


Many researchers give importance to feature selection and overlook the answer designed for the system.

from this study, it indicates that every process is important to the system.


In learning, there are four classifiers integrated in SUCRE: Decision tree, Nave bayes, Support vector machine and maximum entropy.

finally the best reported results were achieved with Decision tree.


Instances are created based on mention to be resolved and an antecedent candidate.

those models suffer from an important weakness.


Sonny Bono was first a songwriter in the nineteen sixties and then a popular entertainer."

there exist some important differences between the dependency and constituent parse tree structures.


Experiments on the ACE 2003 corpus showed that this joint ILP formulation improved the F1-measure by 3.7-5.3 on various domains.

their experiments assume true ACE mentions (i.e.


For SVM and TSVM, the increase of labeled instances means more labeled data are available, so the classifiers perform better.

for label propagation when more seeds take part in the label propagating process, unlabeled vertices could get more reliable sources so that LP could become more confident to decide the label one specific vertex belongs to.


The bi-gram "实际(…)出发" bears a higher co-occurrence frequency from the corpus based on statistical models.

it is syntactically ill-formed as a noun/verb phrases.


(Wu and Zhou, 2003) are the first researches to extract synonymous collocations by mapping synonyms relationships between two different languages to automatically acquire English synonymous collocations.

this method needs a parallel corpus which is difficult to be obtained in real case.


On the one hand, if the object can move past the verb, deriving the verb-final property, then it may move further leftwards to a structurally higher position above the subject.

if the first move past the verb fails, then it is impossible for it to move to a structurally higher position.


Third, the better performance of female group in both tasks may reflect that female participants tend to be more sensitive to the exam stress.

male learners might be less driven by the fixed time for interpreting and less affected by the environment.


The dependency structure used here is in fact quite analogous to the use of abstract syntax tree in most of the compiler implementation.

we note that the SSTCs can easily be extended to keep multiple levels of linguistic representation (e.g.


The wheat straw in (9) changes its state when it is shattered.

"shattered" is not an absolute specification, but can be specified "in greater degrees of accuracy" (Gruber 1985: 82).


second delimiter 'to 3-8cm long' can occur and provide more precise information for the event of cutting.

unlike English, in addition to the delimiter that further specifies the endpoint/endstate, Chinese allows another type of delimiter to follow an existing delimiter and function to reinforce the attainment of the endpoint/endstate denoted by first delimiter.


Mandarin Chinese (hereafter "Chinese") often expresses directed motion events through a concatenation of verbal morphemes, e.g., gun "roll" and jin "enter" in gun-jin shui-li "roll into the water".

1 the order of these co-occurring motion morphemes is not flexible, e.g., *jin-gun shui-li enter-roll water-inside.


(1) a. huapo chu buduan you xuanshi gun-luo landslide place continuously have hanging.stone roll-fall "There were hanging stones continuously rolling and falling from the place of landslide" (PKU Corpus) 2 b. na-kuai tiaoshi gun-jin-le yan-xia de shui-jian that-CLF square.stone roll-enter-ASP cliff-below REL water.stream "That square stone rolled into the stream under the cliff."

(PKU Corpus) the RVC account cannot explain why when luo "fall" and jin "enter" co-occur, only luo precedes jin (2a), but not vice versa (2b), given that both morphemes can have a result understanding, as (1a) and (1b) illustrate.


The hierarchy is able to account for a large amount of data in Chinese, as shown in the corpus studies by Lin and Peck.

3 Lin and Peck do not provide explanation why this hierarchy emerges; in addition, the hierarchy is a generalization of the morpheme order in existing Chinese motion constructions, but it does not explain why some combinations of motion morphemes do not exist in Chinese although they are allowed by the hierarchy.


Lin (to appear) points out that although dao "arrive" lexicalizes a closed scale, it does not specify information about the direction in which a moving object moves to the reference object, nor what kind of reference object is involved in the motion event.

the other two-  point closed scale motion morphemes such as jin "enter" and chu "exit" are highly specific about the reference object and path: jin "enter" expresses a boundary-crossing motion event involving motion from the outside to the inside of an enclosed region, whereas chu "exit" expresses a similar event in a reverse direction.


Hengeveld 1992: 32), verbs with predicative complement are part of the predicate, determining at least partly the content of the event and argument structure.

differently from "regular" predicative verbs, in order to reach a complete semantic and syntactic independence they need a predicative complement.


When it is used with a predicative complement, the contribution of the verb rimanere is mainly aspectual in nature.

the aspectual properties of the predication are determined compositionally, depending also on the characteristics of the predicative complement.


Lakoff 1966), which usually cannot occur in these contexts.

with reference to verb aspect, it has been noted (among others, by Dowty 1979: 183-184, Levin-Rapppaport Hovav 2005) that classical stativity tests should instead be used to individuate non-agentive verbs.


The idea of using metaphoric mappings as explicit instructions to facilitate domain linking processes between L1 and L2 figurative concepts not only follows the trend of cognitive linguistics but also deals with cultural gaps by utilizing learners' already-existent world knowledge and universal concepts.

up till now the idea hasn't been empirically tested yet, and hence hasn't been able to claim its effects on L2 learning.


Regarding the sentences containing metaphoric/metonymic expressions, the CM group showed no significant progress whereas the MM group showed significant differences between two tests.

regarding the sentences containing no metaphoric/metonymic expressions, both groups performed no significant differences between the two tests.


The CM-group participants did not make significant progress on any of the four categories, though they received higher scores averagely in the posttest.

the MM-group participants made significant progress in Category Three (C3) and Category Four (C4).


In other words, the improvements on mean scores of the tests give positive evidence of beneficial effects on cognitively-based instructions, including CM and MM instructions.

when comparing the participants' performances of the two groups, the degree of progress the MM group made was lower than the degree of progress the CM group made in judging sentences with or those without metaphors/metonymies, as shown in Table 3.


In previous research, we conducted experiments on these two models and obtained better results from the generative model (Roh, 2009).

in analyzing the errors in detail, we found that the dependency weight of the bilexical model has some problems.


The usual dependency accuracy includes the match of the relation between the head and its dependents (Lin, 1998).

we do not consider the tags or any relation due to the tag disagreement and bracketing style.


In brief, the caused-motion construction includes a directional phrase like into PP and entails a movement feature.

(5) illustrates an atypical example of caused-motion construction where the construction fails to be interpreted through its components or what the PP attachment problem is based on.


In addition, the rate of V-NP or NP-PP occurrence may also interfere with the feature reading.

pp the determination of feature strength is subjective which requires a more objective approach such as cognitive experiments to be conclusive; therefore, we designate this category as 'undetermined'.


(11) Typically, Mr. Grace inducts his Catholic recruits into the Knights of Malta and encourages their participation in charity.

(WSJ-V855) if the WordNet result did not have a match with the verb under search, dictionaries (e.g., Merriam-Webster online dictionary and Collins English Dictionary) were consulted.


(14a) It happened in the 1970s when the government panicked itself into an "energy crisis," (WSJ-V202) (14a) …deluded ourselves into thinking we were safe.

(WSJ-V70) the result for cognition verbs is rather difficult for interpretation as the category could have covered too broad a range.


It is therefore worthy of further consideration in NLP tasks involving PP-attachment.

there are some limitations to the feature specification approach of this study.


Therefore, these words (removed the stopwords) are chosen to represent the organization.

some organization webpages are created by java-scripts or even flash, no text information could be extracted from them at present.


Parallelism has the computational advantage that the grammars can be used in similar applications and that machine translation (Frank 1999) can be simplified.

parGram also allows flexibility where parallelism is not maintained when different analyses are desirable and justified for good languagespecific reasons.


We want the grammar to be able to give us the most wanted parse(s), reducing unintended ones.

at the same time, we want the grammar to be able to recognise and maintain natural ambiguity, as demonstrated in cases of multiple readings associated with control structures.


It is interesting to note that whereas Korea and Japan, for example, have adopted the Chinese logographic writing system, and have even incorporated it into basically at one time or another bimodal writing systems.

related languages such as Mongolians and Manchus switched to the Chinese language when they conquered all of China, rather than imposed their own language as the native language, with possible adaptation or adoption of the logographic script.


hankypanky in English, xilihuala 稀里啦 "noisy, messy" in Mandarin, bingling-bamlam "noisy" in Cantonese.

there can be more complex syntactic and semantic parallelism (e.g.


As such, we might conclude that social media data is a foe of NLP, in that it challenges traditional assumptions made in NLP research on the nature of the target text and the requirements for real-time responsiveness.

if we look beyond the immediate text content of social media, we quickly realise that there are various non-textual data sources that can be used to enhance the robustness and accuracy of NLP models, in a way which is not possible with static text corpora.


Moreover, we think that RS and TO achieved the highest accuracy because the two DA methods, Random Sampling and Target Only, were sufficiently different.

similarity Filtering is similar to Target Only when the source and target data are not similar to each other and it is similar to Random sampling when the source and target data are sim-ilar to each other.


be done on the basis of resources that would overcome some clear limitations of the evaluation process adopted in this study.

in spite of the limitations, we believe the results reported in this study can have some implications for future studies, including extending the results to other grammatical functions like subject, or making use of other Korean ontologies like U-WIN or CoreNet.


Mining and drawing a full picture of these relationships can be adopted in many practical fields, such as public health services, drug discovery, etc.

due to the considerable number of unstructured data, it is unrealistic to go through and obtain the panoramas of relationships manually.


Agichtein and Gravano (2000) proposed a system called Snowball, which adopts similar strategy with DIPRE.

snowball does not use exact match, but a similarity function to group similar patterns instead.


The benefit of transition-based parsing is the use of greedy algorithms which have a linear time complexity.

due to the greedy algorithms, longer arc parses can cause error propagation across each transition (Kübler et al., 2009).


Firstly, McGloin (1985) also suggests, albeit very briefly, a pragmatic analysis of expressivity.

in her analysis, neither situation nominalization nor the context-dependency of expressivity is treated.


Another approach (C. Banea.et.al, 2010) used multilingual space and meta classifiers to build high precision classifiers for subjectivity classification.

aforementioned work (C. Banea.et.al, 2008) concentrated more on language specific attributes due to variation in expression of subjectivity in different languages.


Structural and lexical style markers can be considered as stylistic features which has shown good results in Web discourse (A. Abbasi.et.al, 2008).

style markers have seen limited usage in sentiment analysis research.


Nonetheless, the MPQA corpus does not distinguish different types of private states, such as Affect and Judgment, which can provide useful information in sentiment analysis.

the MPQA corpus distinguished different ways that private states may be expressed, i.e.


In many languages, words are separated by spaces.

words in Mandarin cannot be discerned orthographically since they are not physically separated.


As Table 1 shows, the 'bu X' strings do not occur frequently in the corpus.

when the expected value was calculated, it was found that the token numbers were larger than expected by chance.


Table 3 shows, bu in bu zhijie and bu hefa can be modified.

in the Sinica Corpus, bu in bu chengshi is not found to be modified.


With coreference information, recall is expected to improve.

the accuracy of coreference resolution systems tend to deteriorate as the distance between the pronoun and the mention increases.


With POS checking only, the system properly detected 166 CSPs but it also detected 89 instances of false positives.

common word exclusion reduced the number of false negatives from 89 instances to 6.


The information from homepage is important.


The weights of lexical features are quite different for different organizations.

when the organization is given, lexical features could distinguish related or unrelated tweets effectively.


This shows link webpage and related webpage give more information about the given organization, our proposed web sources is effective for this task.

the performance of classifier built with the features gotten only from homepage is not as good as expectation.


They use statistical features such as term frequency, common n-gram length, and question answer length ratio.

to our approach which tries to validate the location of a final answer; their work is limited to the retrieval of similar question.


b. Ame, ame, hur-e, hur-e rain rain fall-IMP fall-IMP "Rain, fall!"

(Kageyama, 1993) as Matsumoto (2000) correctly points out, unaccusative imperatives can be used to represent a wish of the speaker, and it is exactly the case with (18).


One might predict that the nominative case particle in the sentences (10a) and (10b) can be substituted for the genitive case particle via the NGC.

this prediction is not correct: The unacceptable sentences (16) above suggest that not only the nominative case particle with adjunct PP, but also the nominative case particle with adjunct NP cannot undergo the NGC.


They note that the unigram model outperforms all other models.

the unigram model isn't suitable for Chinese microblog post, and we make full use of new emoticons which appear frequently in Chinese micro-blog post.


Phrase structure tree is divided into several sub tree by the punctuations, starting from each sub tree, we find out the shortest phrase path, and determine emotional orientation of the topic.

fig.2 Phrase structure tree of micro-blog example the negative word makes the internal sentiment words tend to shift.


Using sentiment words to determine emotional orientation plays an important role for sentiment analysis.

as for micro-blog post, the effects of internet slang and emoticons are more significant, the result is described in Table 7.


Since the purpose of the classification in this paper is to identify long-term customers who have purchased the product several times, we classify such name as the temporal class.

the product name with 'no article' such as (10) or (12) should be classified as the atemporal class as it is just used in order to describe a generic object not an product instance.


0 1 8) Subject-Verb in a clause with "se" 0 1 9) Subject-Verb with "gustar"-like verbs 0 1 Total 5 11  Out of the 29 flagged errors by the grammar, there have been 12 false positives.

3 of them were due to misspellings in the learner corpus, 1 to syntactic errors, 4 to parser errors, and 4 of them are "real" false alarms due to the grammar.


In the meaning point, translations by RBMT (second column) have not bad accuracy.


The experimental system achieves positive results on the domain of tourism and also shows the correctness of the model.

the statistic relation impacts on the system precision and executed time is depended on network speed.


Furthermore, we also built an open framework for building an automatic question answering system.

the system still remains some limitations due to the lack of amount of training question dataset as well as pessimistic rank algorithms for returned answers.We recommend the knowledgebased method to acquire the most remarkable performance and F1 score.


(Li et al., 2009) made use of the constraint that one Pinyin can only map to limited number of Chinese characters thus greatly reducing the computation cost.

their results show that CRF model does not outperform ME model (Li et al., 2009) and the CRF training will cost about approximately 200 days.


From this graph, it is apparent that the ME model decoding is slightly faster than the SMT model as the sentence is quite long.

for most sentences with 20 to 40 characters, the SMT model does not decodes slower than the ME model.


One of the reasons might be that Wakamono Kotoba has been usually treated as "improper expression" or "bad word" (Noguchi , 2004).

considering that these words are getting more and more frequently used on WWW, it is inevitable to treat Wakamono Kotoba even though they are improper expressions.


This result suggested that special expressions such as Wakamono Kotoba should have some potential to contribute to express emotion.

if we use Wakamono Kotoba as feature for probabilistic classification approach, Waka-  mono Kotoba might result in decreasing the estimation accuracy because the frequency of Wakamono Kotoba is low.


The details of the WKRC is shown in  WKRC, there were some example sentences whose substrings matched Wakamono Kotoba.

instead of manual correction, we automatically removed HTML tags and too short or too long sentences 7 .


With respect to a given corpus, we call questions with the following property Single Passage Evidence Questions or SEQs: A question Q is a SEQ if evidence E sufficient to select A as an answer to Q can be found in the same text snippet as A.

we call a question that requires multiple different pieces of evidence (in multiple text spans with respect to a corpus) a Cross-passage Evidence Question or CEQ: A question Q is a CEQ if the set of evidence E1, ..., En needed to justify A as an answer to Q cannot be found in a single text snippet containing A, but only in a set of such snippets.


The results show that the strategy is successful to a degree in that some questions are indeed correctly answered but it also comes with a cost of multiplying the number of questions.

direct Answer Retrieval method is processwise more efficient, has a better performance in terms of accuracy, and does not require tricky question factoring regarding this type of questions.


Typically, the TLTAG Selection process selects all elementary TLTAG trees from the TLCS-TLTAG Resource in which their TLCSs precisely agree with the TLCS input.

in this case, the Lexical Replacement pattern forces the process to specifically choose the trees not just only whose TLCSs are identical to the TLCS input but also whose syntactic structures are the same as that of the TLCS input tree.


As for the next three paraphrasing techniques, the ideas behind each technique along with its operating procedure will be briefly discussed in this section.

the examples of these techniques will be collaboratively demonstrated in Section 4 to show how these techniques can be used in combination to generate more complex sentence paraphrases.


A significant amount of discussion about anaphora in Hindi is available in literature.

in this section, we discuss the categorization of anaphoric relation and pronouns in Hindi that are considered while taking decisions regarding the annotation in this project.


To be able to mark the above mentioned constituents, the scheme must support annotation of multiple referents for an anaphora.

such cases can not be handled by schemes like MUC that use simple co-indexing and marking of continuous spans.


The referent of the pronoun NP6 ( )(It) is ( a )(India's economy).

head of the span NP2 (a )(economy) has two modifiers NP1 ( ) (India's) and VGNF1 ( )(falling) as shown in the diagram below : nmod only NP1 is required as a modifier of NP2 for the correct interpretation of the pronoun.


Considering sequential annotation in above example, NP1( )(Ram) would be selected as the referent of NP2(a )(his).

reflexive pronoun NP2(a )(his) is bound to NP3(u )(he.ACC), thus it would be linguistically justified to select NP3(u )(he.ACC) as the referent.


We divide the study in two parts as follows : As stated in Section 2, only Concrete reference types were annotated in the first phase of the annotation.

in Hindi same lexical pronoun can refer to Concrete as well as Abstract reference entity and many a times it becomes difficult to identify this distinction.


The parsed tree is more powerful in capturing the sentence structure.

it is expensive to create tree structure, and building a good quality parser is also a hard task.


The following comment particularly highlights the relevance and potential applicability of the model in our investigation of lexical sensitivity in WSD: "… It is possible that words rated low in context availability largely possess context-dependent knowledge which is relatively inaccessible when the words are presented in isolation.

when such words are presented in supportive contexts, this context-dependent information becomes highly available for deriving meaning, eliminating potential differences in comprehension between abstract and concrete words."


The sketch differences show that Sense 1 has a lot of common patterns with non-Sense 1 data.

it is the most frequent sense and might therefore have an advantage.


However, it is the most frequent sense and might therefore have an advantage.

sense 4 has few common patterns with other senses but has considerable distinct patterns of its own with regard to local collocation and syntactic relations.


For instance, nouns are often used in the subject and object positions and thus whether we find a verb before or after the target noun or whether its previous word is a determiner may not be a very good contextual feature in general because the various senses of a given noun may all occur in such similar contexts.

if one sense of the noun tends to appear in very specific constructions, such as in very unique prepositional phrases, then in such cases one can expect local collocations and n-gram combinations to be relatively useful for distinguishing this sense from the others.


For instance, in Perry's story, his pronoun I in (1d) is grammatically specified for referring via his self-notion Perry nself.

other co-referring expressions such as those in (1a)-(1c) do not necessarily involve the self-notion.


You self why not go Q 'Why didn't self (you) go?'

this is not true, for the analysis of sentence free ziji as an adverb may be harmless for cases with ziji in the subject position, but not for cases with ziji in other positions.


For instance, in Mapun, the non-de se mode can never be associated with say, though it is possible with other predicates.

according to our judgment of the relevant Chinese data, we do not think this generalization holds in Chinese.


Zhangsan, as Lisi's teacher, was very happy to hear this news.

lisi actually is Zhangsan's son, though he does not know this, because his son was lost at the age of three years old, and then was adopted by another family that he has never met.


Second, Wechsler's DRT framework for person indexicals is inadequate to characterize the belief reports involving the first/second person pronoun in the embedded clauses, though it works well for single-clause sentences.

for instance, the single-clause sentence 'I am smart' uttered by Zhangsan can be characterized as follows: (20) speaker: Conceives (Zhangsan, < z ismart, z nself>, smart' ' ' '(Zhangsan)) the belief report with an embedded clause 'Lisi believes that I am smart' uttered by Zhangsan apparently can't be analyzed in the same way.


Aspectual markers in serial verb constructions can help determine the head of these constructions (Li, 1991; among others).

the majority of Chinese serial verb constructions do not have overt aspectual markers.


(Li, 1991:104 (11)) b. ta na-le dao qie rou he take-ASP knife cut meat 'He took the knife to cut meat.'

(Li, 1991:112 (13a)) problems still exist because not all Chinese SVCs are overtly marked with aspectual markers.


The verb suffixed with the perfective marker le or the experiential marker guo is often treated as the head of a construction, both syntactically and semantically, as in (4) (Li, 1991).

as for the verb that is suffixed with the durative marker zhe in an SVC, we argue that the VP containing the verb functions as an adverbial to modify the other VP (unless the verb in the other VP is also suffixed with zhe) (cf.


Japanese dare-mo has been widely acknowledged to be an NPI, furthermore, a "strict" NPI in the sense of Giannakidou (2011) as it seems to be licensed only in an "antiveridical" environment, specifically, with a clausemate negation.

there is a type of positive sentences in which dare-mo can appear, i.e.


(1) (Adopted from Haraguchi 1995: (6) In Tokyo Japanese, the accent is placed on the mora before the pitch drop; in other words, the accent is on the H immediately before L. (Haraguchi 1999: 5) As dare-mo in question has the same tone melody as (1c), as is shown in (2), it is considered to be an unaccented word.

( 2)The tone melody of "NPI" dare-mo dare-mo L H H the near-homonymous, "non-NPI" dare-mo has the tone melody as illustrated in (3), which is of the same pattern as in (2a).


Kato 1985, Kawashima 1994, Kishimoto 2008.

there is a type of sentences questioning the legitimacy of dare-mo as a genuine NPI.


Among the analyses proposed along the line are Kato (1985), Kawashima (1994) , and Kishimoto (2008).

given that dare-mo can occur in a non-negative sentence as in (6) and the resulting sentence is of a universal-quantificational force, dare-mo as a quantifier should be taken to be a universal one instead of an existential one; consequently, the logical structure of the "nobody"-interpretation associated with a negative dare-mo sentence should be construed as a universal quantifier over negation, ∀¬ instead of negation over an existential quantifier, ¬∃.


Yesterday-Gen earthquake Loc many people-Erg die-3 pfv 'Many people died in yesterday's earthquake.'

it takes cognate object like other unergative verbs as illustrated in the following example, where 'maut' is the cognitive object variant of the verb marnaa 'die' (10) wo kutte ki maut marA.


Although Class2 has considerable intra-class variability, but the small percentage of verbs in that class doesn't affect the overall MSE value.

the unaccusative class has high intra-class variability, and having a high number of intransitive verbs, it affects the MSE value significantly.


In response to the observations, we use the model to show that Class2 samples are indeed hard to classify with maximum misclassification rate.

class1 and class3 verbs are classified quite well with a low misclassification rate.


In recent years, e-commerce is widely used throughout the world and it enables people to purchase products from foreign countries.

sometimes it is not easy for foreign buyers to find the products they want because of the language difference.


Here, the Alphabet Queries are the keywords which were actually used by non-Japanese user on a Japanese website and the Correct Queries were transcribed by volunteers.

some pairs of them were not transliterated into Japanese phonogram, i.e., Katakana or Hiragana; they also have some free translations or Chinese characters.


(2011) developed the English-Chinese transliteration, which consists of many-to-many alignment and the CRF (conditional random fields) using accessor variety.

as far as we know, the transliteration using noisy query data has not been accomplished so far.


Here, we filtered out the pair which has alphabetic or Chinese character Correct Queries to refine the pair corpus more (CAF: The shaded data with light gray and the shaded data with gray were removed).

if we filter out too many query pairs to improve the quality of the training data, we may not be able to obtain enough training data for the probabilistic methods or machine learning.


When the system considered the transliteration of M, it used the P in "S UW1 P ER0", which is two phonemes before M, as a feature.

this "P" is unrelated with "M AA1 R IY0 OW0".


Although both datasets contain multiple participants, they differ in the nature of their content; thus, we found problems applying the DA set from Forsyth (2007) directly to the library chat forums.

we observed that there is overlap between the two sets of dialogue acts (e.g.